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The hazard matrix with an intervention after 2025: (a) a cohort effect, (b) a period effect. The values correspond to a Weibull distribution W(x;λ=10,k=2)$W( {x;\lambda = 10,k = 2} )$ before the intervention and W(x;12,2)$W( {x;12,2} )$ afterward. Underlying data for this figure can be found in Supporting Information S1.
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Product life extension is often portrayed as one of the pillars of the circular economy since longer lifetimes slow down material turnover rates and thus decrease resource use and associated emissions. Strategies for product longevity can involve addressing the product “nature” (inherent product durability) or “nurture” (external factors). Yet, in...
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This study employs a material flow analysis (MFA) to examine the usage patterns of domestic and imported wood resources in South Korea, analyzing the significant differences in utilization, and their impacts on carbon storage. We found a contrast between manufacturing and industrial uses of domestic and imported wood. Imported wood products with hi...
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... While flows can be more volatile, stocks are generally more stable, reflecting long-term patterns of socioeconomic metabolism (Müller, 2006), given their direct involvement in providing services fulfilling many human needs (Pauliuk & Müller, 2014). Lifetime is usually expressed as a parametric distribution function describing the time delay between inflows and outflows (Baccini & Bader, 1996;van der Voet et al., 2002) or between stocks and outflows (Krych et al., 2024), so it determines the dynamics of the system's stocks and flows. For example, assuming a constant lifetime, if the inflow levels increase, the materials accumulate faster and cause stock growth. ...
... The model was developed in the programming language Python using the ODYM framework (Pauliuk & Heeren, 2019) with the dynamic lifetime extension (Krych et al., 2024). The created dMFA model was inflow-driven, meaning that inflow and lifetime inputs serve to calculate the outflow and stock. ...
... In this work, we used a time-based approach to lifetime change, which reflects the "nurture" of product lifetimes and can be contrasted with the cohort-based approach, widespread in dMFA, reflecting the "nature" of product lifetimes (Krych et al., 2024). Our time-based perspective means that the lifetime is varied by time, which brings immediate effects on the stock-flow dynamics, allowing us to directly link the lifetime change to other changes in the system, for example, the increase in sales levels. ...
Longer lifetimes of consumer products are promoted as an element of sustainable consumption, yet there is a widespread notion that lifetimes are currently in decline, often attributed to planned obsolescence or throwaway mentality. However, empirical evidence is inconclusive and often subject to high uncertainties. Here, we explore long‐term trends in the lifetimes of large household appliances using dynamic material flow analysis. We investigate the sales and ownership of these products since their introduction in Norwegian households and use this co‐evolution to estimate the lifetimes. By combining two model types with uncertainty analysis, we show that a significant lifetime decrease was likely experienced only by washing machines (–45%) and ovens (–39%) around the 1990s–2000s. This finding challenges the narratives about planned obsolescence despite their prevalence decreasing consumer incentives for longer product use and repair. We suggest multiple technical, economic, and social factors that could be responsible for the decrease, for example, a reduction in relative prices of appliances or changes in habits surrounding laundry and kitchen use. Our results suggest that factors affecting product lifetimes are not uniform but context‐dependent, which has implications for lifetime extension policy. The presented method could help monitor the long‐term effectiveness of such a policy. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.
... This disregards the influence of period effects: external events that can affect multiple cohorts at once during the use phase, such as natural disasters, wars, renovation, or urban policies. However, it is assumed that lifetimes are not only influenced by cohort effects but also by period effects and their combination (Yang & Land, 2013;Krych et al., 2024). To better account for these, the hazard function can be used. ...
... Its use is recommended by Lawless (2003) when factors affecting lifetime vary over time. It has long been applied in the medical field (Cox, 1972;Fine & Gray, 1999;Therneau & Grambsch, 2000) and has been penetrating the field of industrial ecology in recent years (Aguilar Lopez et al., 2022;Miatto et al., 2017;Zhou et al., 2019) (Krych et al., 2024). The flexibility and high granularity of the hazard rates can improve our understanding of the historical dynamics influencing building lifetimes. ...
... Since the likelihood of demolition is only represented as a fixed function of age, the time when buildings exit use is predefined as soon as they enter the stock. Therefore, these distributions lack the flexibility to account for the introduction of events that will affect the lifetime during the use phase (Krych et al., 2024). Our empirical findings ( Figure 5) confirm that demolition activity is highly sensitive to and motivated by external events that occur during the use phase. ...
Despite their relevance in building stock modeling, building lifetimes are poorly understood and tend to form the weakest link in forecasting energy use, greenhouse gas (GHG) emissions, waste generation, and resource use. Here, we develop a methodology to trace building lifetimes for cohorts in two central areas built up after fires in the 1840s. Using Geographical Information System (GIS) data of the current building stock and archival material, we determined yearly hazard rates for buildings within the cohort 1841–1845 in the historical center of Trondheim, Norway. We find that hazard rates are very sensitive to events ranging from global to hyperlocal scales and that demolition rates have slowed down significantly since the 1980s when municipal preservation policies came into effect. In contrast, age‐based lifetime approaches fail to capture the effects of such events as they only account for the delay between construction and demolition. We discuss the use and limitations of hazard rates to better reflect changes in demolition that are not correlated with building age. Our study underscores that building lifetimes are a property of a wider system rather than an attribute of individual structures. In that sense, hazard rates are a more suitable approach to capture spatiotemporal changes of building stocks and could be further used in scenarios in dynamic models. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.
Consumer goods environmental policy is increasingly focusing on product durability and product lifetimeextension (PLE) to reduce their impact. Given the growing societal relevance of PLE, this review investigates thediscourse about its environmental effects, and the empirical knowledge that substantiates this discourse. Onehundred and nine relevant articles were selected from 388 distinctive records identified in two databases, Scopusand Web of Science. The statements about the environmental effects of PLE in these publications were extractedand analysed, and a detailed process of backward citation tracking was followed to identify the empirical basesubstantiating these statements, leading to 85 additional publications that were included in further analyses.The findings show that the main environmental benefits expected from PLE are related to reductions in thevolume of goods produced, which result from expected reductions in demand due to delayed product replace-ment. However, this reasoning is based on two under-researched assumptions about consumer and industrybehaviours: that the demand for new products is driven by replacement, and that decisions on production vol-umes in the industry are driven by consumer demand. The empirical base in the field is dominated by quanti-tative assessments that reproduce these assumptions rather than studying them. The findings from a handful offield studies that investigate the presumed behaviour, question that it applies. Therefore, a research agenda isproposed to better understand the relations between product lifetimes and material flows and the influence ofconsumer and industry behaviour over them. Moreover, given the current gap between the durability discourse and the empirical knowledge that would be needed to substantiate it, recommendations are made for academics, policy makers, advocacy groups, and businesses environmental strategists to moderate their expectations from product longevity measures.
